Liquidity providers deposit funds into Pangolin and receive trading fees from traders (supply-side fees).

Pangolin is a non-custodial exchange protocol built on Avalanche (originally a fork of Uniswap). Pangolin was founded by an anonymous team and launched in 2020.

PNG tokenholders own and govern the protocol. They currently take a cut of the total trading fees paid by traders (revenue).

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Pangolin

# Pangolin Average trading volume (per user) Methodology

## 1. Overview

Trading volume average per user measures the average daily trading volume per daily active user on the Pangolin decentralized exchange protocol. This metric answers the question "How much volume does each active trader generate on average per day?" and is calculated by dividing the total daily trading volume in USD by the number of unique daily active users. The metric provides insight into user engagement intensity and trading behavior patterns on the protocol.

## 2. Data sources

The calculation uses the following raw data sources:
- **Pangolin V1 swap events**: Parsed `Swap` events from Pangolin V1 smart contracts on Avalanche blockchain, containing token amount changes for each trade
- **Trading pair creation events**: Parsed `PairCreated` events from the Pangolin V1 Factory contract, defining all available trading pairs and their token compositions
- **Avalanche transaction records**: Transaction metadata from the Avalanche blockchain providing user addresses, timestamps, and transaction context
- **End-of-day token price feeds**: Daily USD price data for tokens to enable volume conversion from native token amounts to USD values
- **Token metadata**: Token information including symbols, decimal places, and contract addresses for proper amount formatting and identification

## 3. Step-by-step calculation

### Component A: Trading volume calculation

- **Purpose**: Calculates the total daily trading volume in USD across all Pangolin V1 trading pairs
- **Logic**: Extract all `Swap` events from Pangolin V1 pair contracts on Avalanche. For each swap, calculate the net change in both tokens by subtracting outgoing amounts from incoming amounts (`amount0In` minus `amount0Out`, `amount1In` minus `amount1Out`). Join with trading pair metadata to identify the specific tokens involved and their decimal places. Convert raw token amounts to human-readable format by taking absolute values and dividing by 10 raised to the power of each token's decimals. Convert both token amounts to USD using end-of-day price feeds. Apply price manipulation protection by taking the lesser USD value between the two tokens in each swap. Aggregate all swap volumes daily across all trading pairs and users.
- **Key fields**: `amount0In`, `amount0Out`, `amount1In`, `amount1Out` from swap events, `token0_address`, `token1_address`, `token0_decimals`, `token1_decimals` from pair metadata, `price_usd` from price feeds

### Component B: Daily active users calculation  

- **Purpose**: Counts the number of unique users who performed trades each day
- **Logic**: Extract user addresses from all swap transactions by joining swap events with Avalanche transaction records to get the transaction originator address. Group by day and count distinct user addresses who initiated at least one swap transaction. Use HyperLogLog sketches to efficiently aggregate unique user counts across different dimensions while maintaining accuracy for large datasets. Generate multiple aggregation views including project-level totals, chain-level breakdowns, user type breakdowns, and product-level breakdowns.
- **Key fields**: `user_address` from transaction records, `block_timestamp` for daily grouping, HyperLogLog sketch functions for efficient unique counting

### Final aggregation

- **Combining components**: Join the daily trading volume data with the daily active user data on the same timestamp, using the project-level DAU aggregation (dimension equals 'project_dbt_tag') to get total unique users across all Pangolin activity
- **Currency conversion**: All trading volume is converted to USD using end-of-day price feeds matched by token address, chain, and date  
- **Time aggregation**: Both components are aggregated to daily granularity using timestamp truncation to day boundaries
- **Final calculation**: Divide the total daily trading volume in USD by the number of daily active users using safe division to handle days with zero active users, resulting in the average trading volume per user in USD per day

## Critical guidelines

The methodology includes several important safeguards:
- Price manipulation protection by taking the lesser USD value between paired tokens in each swap
- Safe division to handle edge cases where daily active users might be zero
- HyperLogLog sketches for efficient and accurate unique user counting at scale
- Proper decimal handling using token-specific decimal places from metadata
- Daily boundary consistency between volume and user calculations

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Average trading volume (per user)

Trading volume divided by daily active users.

PangolinPNG

Average trading volume (per user)

Average trading volume (per user)

Competitive landscape